Reinforcement learning : an introduction (Book, 2018) [WorldCat.org]
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Reinforcement learning : an introduction

Author: Richard S Sutton; Andrew G Barto
Publisher: Cambridge, Massachusetts ; London, England : The MIT Press, [2018] ©2018
Series: Adaptive computation and machine learning.
Edition/Format:   Print book : English : Second editionView all editions and formats
Summary:
"Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."--
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Details

Document Type: Book
All Authors / Contributors: Richard S Sutton; Andrew G Barto
ISBN: 9780262039246 0262039249
OCLC Number: 1043175824
Description: xxii, 526 pages : illustrations (some color) ; 24 cm
Contents: Machine generated contents note: 1. Introduction --
1.1. Reinforcement Learning --
1.2. Examples --
1.3. Elements of Reinforcement Learning --
1.4. Limitations and Scope --
1.5. An Extended Example: Tic-Tac-Toe --
1.6. Summary --
1.7. Early History of Reinforcement Learning --
2. Multi-armed Bandits --
2.1.A k-armed Bandit Problem --
2.2. Action-value Methods --
2.3. The 10-armed Testbed --
2.4. Incremental Implementation --
2.5. Tracking a Nonstationary Problem --
2.6. Optimistic Initial Values --
2.7. Upper-Confidence-Bound Action Selection --
2.8. Gradient Bandit Algorithms --
2.9. Associative Search (Contextual Bandits) --
2.10. Summary --
3. Finite Markov Decision Processes --
3.1. The Agent-Environment Interface --
3.2. Goals and Rewards --
3.3. Returns and Episodes --
3.4. Unified Notation for Episodic and Continuing Tasks --
3.5. Policies and Value Functions --
3.6. Optimal Policies and Optimal Value Functions --
3.7. Optimality and Approximation --
3.8. Summary --
4. Dynamic Programming Note continued: 4.1. Policy Evaluation (Prediction) --
4.2. Policy Improvement --
4.3. Policy Iteration --
4.4. Value Iteration --
4.5. Asynchronous Dynamic Programming --
4.6. Generalized Policy Iteration --
4.7. Efficiency of Dynamic Programming --
4.8. Summary --
5. Monte Carlo Methods --
5.1. Monte Carlo Prediction --
5.2. Monte Carlo Estimation of Action Values --
5.3. Monte Carlo Control --
5.4. Monte Carlo Control without Exploring Starts --
5.5. Off-policy Prediction via Importance Sampling --
5.6. Incremental Implementation --
5.7. Off-policy Monte Carlo Control --
5.8.*Discounting-aware Importance Sampling --
5.9.*Per-decision Importance Sampling --
5.10. Summary --
6. Temporal-Difference Learning --
6.1. TD Prediction --
6.2. Advantages of TD Prediction Methods --
6.3. Optimality of TD(0) --
6.4. Sarsa: On-policy TD Control --
6.5.Q-learning: Off-policy TD Control --
6.6. Expected Sarsa --
6.7. Maximization Bias and Double Learning Note continued: 6.8. Games, Afterstates, and Other Special Cases --
6.9. Summary --
7.n-step Bootstrapping --
7.1.n-step TD Prediction --
7.2.n-step Sarsa --
7.3.n-step Off-policy Learning --
7.4.*Per-decision Methods with Control Variates --
7.5. Off-policy Learning Without Importance Sampling: The n-step Tree Backup Algorithm --
7.6.*A Unifying Algorithm: n-step Q(u) --
7.7. Summary --
8. Planning and Learning with Tabular Methods --
8.1. Models and Planning --
8.2. Dyna: Integrated Planning, Acting, and Learning --
8.3. When the Model Is Wrong --
8.4. Prioritized Sweeping --
8.5. Expected vs. Sample Updates --
8.6. Trajectory Sampling --
8.7. Real-time Dynamic Programming --
8.8. Planning at Decision Time --
8.9. Heuristic Search --
8.10. Rollout Algorithms --
8.11. Monte Carlo Tree Search --
8.12. Summary of the Chapter --
8.13. Summary of Part I: Dimensions --
9. On-policy Prediction with Approximation --
9.1. Value-function Approximation --
9.2. The Prediction Objective (VE) Note continued: 9.3. Stochastic-gradient and Semi-gradient Methods --
9.4. Linear Methods --
9.5. Feature Construction for Linear Methods --
9.5.1. Polynomials --
9.5.2. Fourier Basis --
9.5.3. Coarse Coding --
9.5.4. Tile Coding --
9.5.5. Radial Basis Functions --
9.6. Selecting Step-Size Parameters Manually --
9.7. Nonlinear Function Approximation: Artificial Neural Networks --
9.8. Least-Squares TD --
9.9. Memory-based Function Approximation --
9.10. Kernel-based Function Approximation --
9.11. Looking Deeper at On-policy Learning: Interest and Emphasis --
9.12. Summary --
10. On-policy Control with Approximation --
10.1. Episodic Semi-gradient Control --
10.2. Semi-gradient n-step Sarsa --
10.3. Average Reward: A New Problem Setting for Continuing Tasks --
10.4. Deprecating the Discounted Setting --
10.5. Differential Semi-gradient n-step Sarsa --
10.6. Summary --
11.*Off-policy Methods with Approximation --
11.1. Semi-gradient Methods --
11.2. Examples of Off-policy Divergence Note continued: 11.3. The Deadly Triad --
11.4. Linear Value-function Geometry --
11.5. Gradient Descent in the Bellman Error --
11.6. The Bellman Error is Not Learnable --
11.7. Gradient-TD Methods --
11.8. Emphatic-TD Methods --
11.9. Reducing Variance --
11.10. Summary --
12. Eligibility Traces --
12.1. The A-return --
12.2. TD(A) --
12.3.n-step Truncated A-return Methods --
12.4. Redoing Updates: Online A-return Algorithm --
12.5. True Online TD(A) --
12.6.*Dutch Traces in Monte Carlo Learning --
12.7. Sarsa(A) --
12.8. Variable A and ry --
12.9. Off-policy Traces with Control Variates --
12.10. Watkins's Q(A) to Tree-Backup(A) --
12.11. Stable Off-policy Methods with Traces --
12.12. Implementation Issues --
12.13. Conclusions --
13. Policy Gradient Methods --
13.1. Policy Approximation and its Advantages --
13.2. The Policy Gradient Theorem --
13.3. REINFORCE: Monte Carlo Policy Gradient --
13.4. REINFORCE with Baseline --
13.5. Actor-Critic Methods Note continued: 13.6. Policy Gradient for Continuing Problems --
13.7. Policy Parameterization for Continuous Actions --
13.8. Summary --
14. Psychology --
14.1. Prediction and Control --
14.2. Classical Conditioning --
14.2.1. Blocking and Higher-order Conditioning --
14.2.2. The Rescorla-Wagner Model --
14.2.3. The TD Model --
14.2.4. TD Model Simulations --
14.3. Instrumental Conditioning --
14.4. Delayed Reinforcement --
14.5. Cognitive Maps --
14.6. Habitual and Goal-directed Behavior --
14.7. Summary --
15. Neuroscience --
15.1. Neuroscience Basics --
15.2. Reward Signals, Reinforcement Signals, Values, and Prediction Errors --
15.3. The Reward Prediction Error Hypothesis --
15.4. Dopamine --
15.5. Experimental Support for the Reward Prediction Error Hypothesis --
15.6. TD Error/Dopamine Correspondence --
15.7. Neural Actor-Critic --
15.8. Actor and Critic Learning Rules --
15.9. Hedonistic Neurons --
15.10. Collective Reinforcement Learning --
15.11. Model-based Methods in the Brain Note continued: 15.12. Addiction --
15.13. Summary --
16. Applications and Case Studies --
16.1. TD-Gammon --
16.2. Samuel's Checkers Player --
16.3. Watson's Daily-Double Wagering --
16.4. Optimizing Memory Control --
16.5. Human-level Video Game Play --
16.6. Mastering the Game of Go --
16.6.1. AlphaGo --
16.6.2. AlphaGo Zero --
16.7. Personalized Web Services --
16.8. Thermal Soaring --
17. Frontiers --
17.1. General Value Functions and Auxiliary Tasks --
17.2. Temporal Abstraction via Options --
17.3. Observations and State --
17.4. Designing Reward Signals --
17.5. Remaining Issues --
17.6. Experimental Support for the Reward Prediction Error Hypothesis.
Series Title: Adaptive computation and machine learning.
Responsibility: Richard S. Sutton and Andrew G. Barto.

Abstract:

"Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."--

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